![earth imaging earth imaging](http://www.traceybell.co.uk/wp-content/uploads/2016/10/man-in-space.jpeg)
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observe an Earth-sized planet at an Earth-like distance from a Sun-like star, we would need to block out the Sun-like star's light to about 1 part in 10-to-100 billion. s2_sr_cld_col_eval_disp = s2_sr_cld_col_eval.This artist's impression of the Nu2 Lupi planetary system shows three exoplanets. Give the system some time to render everything, it should take less than a minute. Map the add_cld_shdw_mask function over the collection to add mask component bands to each image, then display the results. Return ee.ImageCollection(ee.Join.saveFirst('s2cloudless').apply(**, # Join the filtered s2cloudless collection to the SR collection by the 'system:index' property. S2_cloudless_col = (ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY') filter(ee.Filter.lte('CLOUDY_PIXEL_PERCENTAGE', CLOUD_FILTER))) S2_sr_col = (ee.ImageCollection('COPERNICUS/S2_SR') def get_s2_sr_cld_col(aoi, start_date, end_date): The result is a copy of the SR collection where each image has a new 's2cloudless' property whose value is the corresponding s2cloudless image. Each collection must be filtered similarly (e.g., by date and bounds) and then the two filtered collections must be joined.ĭefine a function to filter the SR and s2cloudless collections according to area of interest and date parameters, then join them on the system:index property. Sentinel-2 surface reflectance and Sentinel-2 cloud probability are two different image collections. If you want to work with a different example, use this Earth Engine App to identify an image that includes some clouds, then replace the relevant parameter values below with those provided in the app. When parameterizing and evaluating cloud masks for a new area, it is good practice to identify a single overpass date and limit the regional extent to minimize processing requirements. The values currently set for AOI, START_DATE, END_DATE, and CLOUD_FILTER are intended to build a collection for a single S2 overpass of a region near Portland, Oregon, USA. Maximum distance (km) to search for cloud shadows from cloud edgesĭistance (m) to dilate the edge of cloud-identified objects Near-infrared reflectance values less than are considered potential cloud shadow Maximum image cloud cover percent allowed in image collectionĬloud probability (%) values greater than are considered cloud Define collection filter and cloud mask parametersĭefine parameters that are used to filter the S2 image collection and determine cloud and cloud shadow identification. This section builds an S2 SR collection and defines functions to add cloud and cloud shadow component layers to each image in the collection.
#Earth imaging how to#
The output will contain instructions on how to grant this notebook access to Earth Engine using your account. Run the following cell to initialize the Earth Engine API.
#Earth imaging code#
Clouds are identified from the S2 cloud probability dataset (s2cloudless) and shadows are defined by cloud projection intersection with low-reflectance near-infrared (NIR) pixels.įor a similar JavaScript API script, see this Code Editor example. This tutorial is an introduction to masking clouds and cloud shadows in Sentinel-2 (S2) surface reflectance (SR) data using Earth Engine.